Querying Dynamic and Context-Sensitive Metadata in Semantic Web Sergiy Nikitin Industrial Ontologies Group 1 University of Jyväskylä Finland Article Authors: Sergiy Nikitin Vagan Terziyan Yaroslav Tsaruk Andriy Zharko 1 – Industrial Ontologies Group web-site: http://www.cs.jyu.fi/ai/OntoGroup
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Querying Dynamic and Context-Sensitive Metadata in Semantic Web
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Querying Dynamic and Context-Sensitive Metadata in Semantic Web
Sergiy Nikitin
Industrial Ontologies Group1
University of Jyväskylä
Finland
Article Authors: Sergiy Nikitin
Vagan Terziyan
Yaroslav Tsaruk
Andriy Zharko
1 – Industrial Ontologies Group web-site: http://www.cs.jyu.fi/ai/OntoGroup
• Story of contextual data querying problem• Contextual Data in Semantic Web• RDQL patterns• Use cases for pattern application in Agent Systems• Conclusions• Further Work
Introduction
• Dynamic, semantically rich data usually contains contextual elements describing conditions under which the data is relevant, useful and up-to-date
• The problem of querying contextual data appeared as a first-year challenge of SmartResource1 project
• Project wider objective is:
– To combine the emerging Semantic Web, Web Services, Peer-to-Peer, Machine Learning and Agent technologies for the development of a global and smart maintenance management environment, to provide Web-based support for the predictive maintenance of industrial devices by utilizing heterogeneous and interoperable Web resources, services and human experts
Use cases for pattern application in Agent Systems
hasBehaviour
rscdfs:predicaterdf:subject
Agent
Behaviour_Statement
rscdfs:trueInContext
rdf:object
Behaviour_Container
Buy TicketsBuy TicketsBuy TicketsBuy Tickets
StatementStatementStatementStatement
rdf:subject rdf:objectrscdfs:predicate
AgentAgent hashas MoneyMoney
rscdfs:Context_SR_Container
Agent ArchitectureAgent Architecture
Resource Resource HistoryHistory
Ontology
Templates
Roles Goals
Behaviour rules
Resource Resource AgentAgent
Behaviour description
Templates
Executable Executable modules or modules or
Web ServicesWeb Services
Conclusions
• Storing and managing context-enabled data via RDF storages is complicated and routine task
• Repeating querying procedures can be organized into reusable querying patterns
• Patterns can consist of other patterns, thus pattern ontology can be developed to represent these relationships
• Patterns correspond to Properties. Property by its range value defines classes of objects which can be referred, hence these objects correspond to certain common structure
Further work
• Further development of Resource Goal/Behaviour Description Framework (RGBDF)
• Querying patterns for RGBDF
• Deeper analysis of Pattern Ontology (how to describe relationships between patterns, how they correlate with Properties)
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